Most theses that stall at chapter four do not have a statistics problem. They have a matching problem: a test chosen because a senior used it, applied to data it does not fit, answering a question the objectives never asked. Fixing that is mostly reasoning, and only partly software.
Assignment Nepal does the analysis for MBS, MBA, MPhil, MPH, nursing, M.Ed. and social-science theses, and for journal papers and project reports, in SPSS, Stata, EViews, R, AMOS, SmartPLS, NVivo and Excel. We use your data, your objectives and the software your department expects.
You get the output, the syntax or script that produced it, tables formatted to your faculty's style, and a written interpretation of every result. That last part is the one examiners test. A panel will point at a table and ask what the number means, and the answer has to come from you.
Similarity and AI writing indicator reports come with every delivery, and we run them on work you wrote yourself too. You get the reports and the explanation, never a bare figure.
We are an independent academic support service, not a university and not a reseller of any detection platform. We run checks and issue the resulting reports; we do not sell access to a detection tool, and we do not sell a way around one.
Tell us what you need checked and when it is due. Your document is checked without being stored in any student-paper repository, so the check never counts against your real submission. The reports come back with what each match means, not just a percentage.
Every test in chapter four should trace back to an objective or hypothesis in chapter one. If an objective is to examine the relationship between two variables, you need correlation, or regression if you are claiming one predicts the other. If it compares groups, you need a t-test or ANOVA. If it tests a theoretical model with latent constructs, you need SEM. When a chapter four contains tests that answer no stated objective, examiners notice, and the usual cause is that the analysis was planned around what the software menu offered.
So the first thing we do is read your objectives and hypotheses next to your questionnaire. Quite often the questionnaire cannot answer one of the objectives as worded. It is far cheaper to discover that before the analysis than at the defence.
Reliability comes first. Cronbach's alpha below about 0.7 on a construct prompts questions, and the most common cause is a negatively worded item that was never reverse-scored, not bad data. Sample size comes second: a sample much smaller than the one the proposal promised needs an explanation in the thesis, not silence.
Then they read the regression or SEM tables, and they expect specific numbers: R² and adjusted R², the F statistic, standardised betas with p-values, and for SEM the fit indices (CFI, TLI, RMSEA, SRMR) or, for PLS-SEM, the loadings, AVE, HTMT and path coefficients. A table that leaves these out, or that still shows SPSS variable names like VAR00003, gets returned.
SPSS handles most master's theses built on a Likert-scale questionnaire: descriptives, reliability, correlation, regression and group comparisons. When the model has several latent constructs and mediating or moderating paths, the work moves to AMOS (covariance-based SEM) or SmartPLS (PLS-SEM). SmartPLS is the usual choice when the sample is modest or the aim is prediction rather than confirming a theory.
Finance and economics theses using secondary data, such as NEPSE prices, bank annual reports or Nepal Rastra Bank series, are a different kind of work. They need time-series or panel methods, which is EViews or Stata territory. The tests there (stationarity, lag selection, cointegration, the Hausman test) have their own order, and skipping a step is a common reason MPhil and MBS finance theses get sent back.
We work only with data you collected or a secondary source you can cite. Datasets are used for your work alone, never shared or reused, and deleted when you ask. We report what the data shows. If a hypothesis is not supported, chapter four says so and the discussion chapter explains why that might be, which panels respect far more than results that look too clean.
Whatever your department expects: SPSS, Stata, EViews, R, AMOS, SmartPLS, NVivo, ATLAS.ti or Excel. If you have no requirement, we pick the one that suits the method and explain why, so you can say the same thing at your defence.
Both. You get the output file, the syntax or script, and the cleaned dataset, so anyone can re-run the analysis and get the same numbers. Tables pasted into a thesis with nothing behind them are hard to defend.
No. We do not fabricate or alter data: no invented responses, no adjusted numbers to make a hypothesis pass. We can help you design the questionnaire, plan the sample and set up Google Forms, but the responses must come from real respondents. If the results do not support your hypothesis, we help you write that up honestly. A non-significant result is still a finding.
Yes. Send the dataset, the output and the comments. We tell you which tests are appropriate and why, re-run what needs re-running, and leave whatever was already right alone.
That is part of every order. You get a plain-language note on each table: what the test does, why it was chosen and what the result means. We can also take you through it on a call before your viva.
Coursework built around code, such as machine learning models, Python notebooks, SQL or dashboards, is handled by our programming and data team at tech.assignmentnepal.org. This page covers statistical analysis for theses and research papers.
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